Date of Award
Industrial and Manufacturing Systems Engineering
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Manufacturing system design can significantly affect the resulting product quality level. Therefore, the early prediction of product quality, as affected by manufacturing system configuration decisions, can enhance the manufacturer's competitiveness through achieving higher quality levels at lower costs in a responsive manner. In this research, a conceptual framework is proposed for the proactive assessment of product quality in terms of the manufacturing system configuration parameters. A new comprehensive model that can be used in comparing different system configurations based on quality is developed using Analytic Hierarchy Process. In addition, a hierarchical fuzzy inference system is developed to model the ill-defined relation between manufacturing system design parameters and the resulting product quality. This model is capable of mapping the considered manufacturing system configuration parameters into a Configuration Capability Indicator (CCI), expressed in terms of sigma capability level, which can be compared to the benchmark Six Sigma capability. The developed models have been applied to several case studies (Test Parts ANC-90 and ANC-101, Cylinder Head Part Family, Gearbox Housing, Rack Bar Machining, and Siemens Jeep Intake Manifold) with different configuration scenarios for illustration and verification. The results demonstrate the capabilities of the CCI in comparing different system configurations from quality point of view and in supporting the decision-making during the early stages of manufacturing system development. The included application of the developed models emphasized that high quality levels can be achieved by investigating all the improvement opportunities and it is recommended that efforts should be directed in the first place to design the system with high defect prevention capability. This can be achieved by using highly capable processes, implementation of mistake proofing techniques, as well as minimizing variability due to parallel processing and variation stack up. Considering the relationship between quality and complexity, it has been concluded that the CCI represents the time-independent real complexity of a system configuration. Furthermore, it has been demonstrated that the product complexity adversely affects the resulting product quality. Therefore, it is recommended that high product quality levels can be achieved not only by using highly capable system configurations, but also by minimizing the product complexity during the design stage.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .N33. Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 4035. Thesis (Ph.D.)--University of Windsor (Canada), 2006.
Nada, Omayma Abdel Aziz, "Quality prediction in manufacturing system design." (2006). Electronic Theses and Dissertations. 3251.